Mol Biol Evol. 2026 Apr 28:msag107. doi: 10.1093/molbev/msag107. Online ahead of print.
ABSTRACT
Genomic prediction leverages all available markers, irrespective of their statistical significance in genome-wide association studies (GWAS). Recent advancements in marker density, sample sizes, and sophisticated statistical GWAS methods have demonstrated that integrating GWAS results can potentially boost the accuracy of genomic predictions. The Genomic Association and Prediction Tool (GAPIT) has recently begun incorporating GWAS findings into its prediction framework, streamlining this approach, referred to as GWAS-Assisted Genomic Best Linear Unbiased Prediction (GAGBLUP). A sufficient simulation study revealed that the benefits of GAGBLUP depend on the GWAS model used. Multiple-locus models, such as Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), outperformed single-locus models, like the mixed linear model. Specifically, when BLINK GWAS results in a real trait were incorporated into genomic Best Linear Unbiased Prediction (GBLUP), prediction accuracy improved by over 20% compared to GBLUP alone. This approach integrates the trait-specific insights from GWAS with the polygenic modeling capacity of GBLUP, resulting in more stable prediction across varying genetic backgrounds. This broader applicability enhances the utility of genomic selection in breeding programs, enabling its deployment across a wider range of crops and trait architectures.
PMID:42047095 | DOI:10.1093/molbev/msag107
